scholarly journals Numbers of close contacts of individuals infected with SARS-CoV-2 and their association with government intervention strategies

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Conor G. McAloon ◽  
Patrick Wall ◽  
Francis Butler ◽  
Mary Codd ◽  
Eamonn Gormley ◽  
...  
Keyword(s):  
Regression Models ◽  
Negative Binomial ◽  
Contact Tracing ◽  
Government Interventions ◽  
Infected Case ◽  
Age Cohorts ◽  
Epidemiological Modelling ◽  
The Impact ◽  
Over Time ◽  
Close Contacts

Abstract Background Contact tracing is conducted with the primary purpose of interrupting transmission from individuals who are likely to be infectious to others. Secondary analyses of data on the numbers of close contacts of confirmed cases could also: provide an early signal of increases in contact patterns that might precede larger than expected case numbers; evaluate the impact of government interventions on the number of contacts of confirmed cases; or provide data information on contact rates between age cohorts for the purpose of epidemiological modelling. We analysed data from 140,204 close contacts of 39,861 cases in Ireland from 1st May to 1st December 2020. Results Negative binomial regression models highlighted greater numbers of contacts within specific population demographics, after correcting for temporal associations. Separate segmented regression models of the number of cases over time and the average number of contacts per case indicated that a breakpoint indicating a rapid decrease in the number of contacts per case in October 2020 preceded a breakpoint indicating a reduction in the number of cases by 11 days. Conclusions We found that the number of contacts per infected case was overdispersed, the mean varied considerable over time and was temporally associated with government interventions. Analysis of the reported number of contacts per individual in contact tracing data may be a useful early indicator of changes in behaviour in response to, or indeed despite, government restrictions. This study provides useful information for triangulating assumptions regarding the contact mixing rates between different age cohorts for epidemiological modelling.

scholarly journals Numbers of close contacts of individuals infected with SARS-CoV-2 and their association with government intervention strategies

2021 ◽  
Author(s):  
Conor G. McAloon ◽  
Patrick Wall ◽  
Francis Butler ◽  
Mary Codd ◽  
Eamonn Gormley ◽  
...  
Keyword(s):  
Regression Models ◽  
Negative Binomial ◽  
Contact Tracing ◽  
Government Interventions ◽  
Age Cohorts ◽  
Epidemiological Modelling ◽  
Contact Rates ◽  
The Impact ◽  
Over Time ◽  
Close Contacts

ABSTRACTBackgroundContact tracing is conducted with the primary purpose of interrupting transmission from individuals who are likely to be infectious to others. Secondary analyses of data on the numbers of close contacts of confirmed cases could also: provide an early signal of increases in contact patterns that might precede larger than expected case numbers; evaluate the impact of government interventions on the number of contacts of confirmed cases; or provide data information on contact rates between age cohorts for the purpose of epidemiological modelling.MethodsWe analysed data from 140,204 contacts of 39861 cases in Ireland from 1st May to 1st December 2020. Only ‘close’ contacts were included in the analysis. A close contact was defined as any individual who had had > 15 minutes face-to-face (<2 m) contact with a case; any household contact; or any individual sharing a closed space for longer than 2 hours, in any setting.ResultsThe number of contacts per case was overdispersed, the mean varied considerably over time, and was temporally associated with government interventions. Negative binomial regression models highlighted greater numbers of contacts within specific population demographics, after correcting for temporal associations. Separate segmented regression models of the number of cases over time and the average number of contacts per case indicated that a breakpoint indicating a rapid decrease in the number of contacts per case in October 2020 preceded a breakpoint indicating a reduction in the number of cases by 11 days.DiscussionThese data were collected for a specific purpose and therefore any inferences must be made with caution. The data are representative of contact rates of cases, and not of the overall population. However, the data may be a more accurate indicator of the likely degree of onward transmission than might be the case if a random sample of the population were taken. Furthermore, since we analysed only the number of close contacts, the total number of contacts per case would have been higher. Nevertheless, this analysis provides useful information for monitoring the impact of government interventions on the number of contacts; for helping pre-empt increases or decreases in case numbers, and for triangulating assumptions regarding the contact mixing rates between different age cohorts for epidemiological modelling.

scholarly journals Spatial and temporal trends in social vulnerability and COVID-19 incidence and death rates in the United States

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248702
Author(s):  
Brian Neelon ◽  
Fedelis Mutiso ◽  
Noel T. Mueller ◽  
John L. Pearce ◽  
Sara E. Benjamin-Neelon
Keyword(s):  
Social Vulnerability ◽  
Primary Care Physicians ◽  
Negative Binomial ◽  
Temporal Trends ◽  
The United States ◽  
Social Vulnerability Index ◽  
Crossover Effect ◽  
Death Rates ◽  
The Impact ◽  
Over Time

Background Socially vulnerable communities may be at higher risk for COVID-19 outbreaks in the US. However, no prior studies examined temporal trends and differential effects of social vulnerability on COVID-19 incidence and death rates. Therefore, we examined temporal trends among counties with high and low social vulnerability to quantify disparities in trends over time. Methods We conducted a longitudinal analysis examining COVID-19 incidence and death rates from March 15 to December 31, 2020, for each US county using data from USAFacts. We classified counties using the Social Vulnerability Index (SVI), a percentile-based measure from the Centers for Disease Control and Prevention, with higher values indicating more vulnerability. Using a Bayesian hierarchical negative binomial model, we estimated daily risk ratios (RRs) comparing counties in the first (lower) and fourth (upper) SVI quartiles, adjusting for rurality, percentage in poor or fair health, percentage female, percentage of smokers, county average daily fine particulate matter (PM2.5), percentage of primary care physicians per 100,000 residents, daily temperature and precipitation, and proportion tested for COVID-19. Results At the outset of the pandemic, the most vulnerable counties had, on average, fewer cases per 100,000 than least vulnerable SVI quartile. However, on March 28, we observed a crossover effect in which the most vulnerable counties experienced higher COVID-19 incidence rates compared to the least vulnerable counties (RR = 1.05, 95% PI: 0.98, 1.12). Vulnerable counties had higher death rates starting on May 21 (RR = 1.08, 95% PI: 1.00,1.16). However, by October, this trend reversed and the most vulnerable counties had lower death rates compared to least vulnerable counties. Conclusions The impact of COVID-19 is not static but can migrate from less vulnerable counties to more vulnerable counties and back again over time.

scholarly journals The impact of political instability upon the increase of terrorism in the Middle East

2021 ◽  
pp. 43-62
Author(s):  
Andrey Vadimovich Novikov
Keyword(s):  
Regression Models ◽  
Negative Binomial ◽  
Political Instability ◽  
Negative Binomial Regression ◽  
Severe Form ◽  
Political Regime ◽  
Level Of Education ◽  
Binomial Regression ◽  
Different Types ◽  
The Impact

The key goal of the article is to examine whether the domestic political instability associated with the &ldquo;Arab Spring&rdquo; caused the subsequent surge of global terrorism, which reached its peak in 2014. The author reviews six different types of domestic political instability: antigovernment demonstrations, national strikes, government crises, government repression, disturbances, and revolutions. Using the regression models, the author clarifies the impact of such factors as the level of education, Internet access, economic development, democratization indexes, and the degree of religious and ethnic fragmentariness. Analysis is conducted on the results of the models separately for different types of political regimes, forms of domestic political instability, and global regions. The results of construction and analysis a number of negative binomial regression models testify to the support of &ldquo;escalation effect&rdquo;, which implies that heightened intensity of domestic political instability leads to the surge of terrorist attacks. More severe forms of domestic political instability, namely repression and disturbances, generate a higher level of terrorism; however, revolution, as the most severe form of domestic political instability does not produce such effect. The formulated conclusions are also substantiated by the fact that certain forms of political instability have a different impact upon terrorism and its peculiarities, depending on the geographical region and the type of political regime.

scholarly journals Estimation of effects of contact tracing and mask adoption on COVID-19 transmission in San Francisco: a modeling study

2020 ◽  
Author(s):  
Lee Worden ◽  
Rae Wannier ◽  
Seth Blumberg ◽  
Alex Y. Ge ◽  
George W. Rutherford ◽  
...  
Keyword(s):  
San Francisco ◽  
Statistical Estimation ◽  
Detection Rate ◽  
Reproduction Number ◽  
Contact Tracing ◽  
Reproduction Numbers ◽  
The Impact ◽  
The City ◽  
Testing Coverage ◽  
Over Time

AbstractThe current COVID-19 pandemic has spurred concern about what interventions may be effective at reducing transmission. The city and county of San Francisco imposed a shelter-in-place order in March 2020, followed by use of a contact tracing program and a policy requiring use of cloth face masks. We used statistical estimation and simulation to estimate the effectiveness of these interventions in San Francisco. We estimated that self-isolation and other practices beginning at the time of San Francisco’s shelter-in-place order reduced the effective reproduction number of COVID-19 by 35.4% (95% CI, −20.1%–81.4%). We estimated the effect of contact tracing on the effective reproduction number to be a reduction of approximately 44% times the fraction of cases that are detected, which may be modest if the detection rate is low. We estimated the impact of cloth mask adoption on reproduction number to be approximately 8.6%, and note that the benefit of mask adoption may be substantially greater for essential workers and other vulnerable populations, residents return to circulating outside the home more often. We estimated the effect of those interventions on incidence by simulating counterfactual scenarios in which contact tracing was not adopted, cloth masks were not adopted, and neither contact tracing nor cloth masks was adopted, and found increases in case counts that were modest, but relatively larger than the effects on reproduction numbers. These estimates and model results suggest that testing coverage and timing of testing and contact tracing may be important, and that modest effects on reproduction numbers can nonetheless cause substantial effects on case counts over time.

scholarly journals Weather Effects on Social Movements: Evidence from Washington, D.C., and New York City, 1960–95

2016 ◽  
Vol 8 (3) ◽  
pp. 299-311 ◽  
Author(s):  
Tony Huiquan Zhang
Keyword(s):  
New York ◽  
New York City ◽  
Social Movements ◽  
Social Movement ◽  
York City ◽  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Future Research ◽  
The Impact

Abstract Scholars have been taking the impact of weather on social movements for granted for some time, despite a lack of supporting empirical evidence. This paper takes the topic more seriously, analyzing more than 7000 social movement events and 36 years of weather records in Washington, D.C., and New York City (1960–95). Here, “good weather” is defined as midrange temperature and little to no precipitation. This paper uses negative binomial regression models to predict the number of social movements per day and finds social movements are more likely to happen on good days than bad, with seasonal patterns controlled for. Results from logistic regression models indicate violence occurs more frequently at social movement events when it is warmer. Most interestingly, the effect of weather is more salient when there are more political opportunities and resources available. This paper discusses the implications and suggests future research on weather and social movement studies.

scholarly journals Analysis of mobility data to build contact networks for COVID-19

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249726
Author(s):  
Katherine Klise ◽  
Walt Beyeler ◽  
Patrick Finley ◽  
Monear Makvandi
Keyword(s):  
Contact Tracing ◽  
Epidemiological Models ◽  
Policy Choices ◽  
Mobility Data ◽  
Single Person ◽  
Rapid Changes ◽  
Network Metrics ◽  
The Impact ◽  
Number Of Customers ◽  
Close Contacts

As social distancing policies and recommendations went into effect in response to COVID-19, people made rapid changes to the places they visit. These changes are clearly seen in mobility data, which records foot traffic using location trackers in cell phones. While mobility data is often used to extract the number of customers that visit a particular business or business type, it is the frequency and duration of concurrent occupancy at those sites that governs transmission. Understanding the way people interact at different locations can help target policies and inform contact tracing and prevention strategies. This paper outlines methods to extract interactions from mobility data and build networks that can be used in epidemiological models. Several measures of interaction are extracted: interactions between people, the cumulative interactions for a single person, and cumulative interactions that occur at particular businesses. Network metrics are computed to identify structural trends which show clear changes based on the timing of stay-at-home orders. Measures of interaction and structural trends in the resulting networks can be used to better understand potential spreading events, the percent of interactions that can be classified as close contacts, and the impact of policy choices to control transmission.

scholarly journals Epidemiology and Transmission of COVID-19 in Shenzhen China: Analysis of 391 cases and 1,286 of their close contacts

2020 ◽  
Author(s):  
Qifang Bi ◽  
Yongsheng Wu ◽  
Shujiang Mei ◽  
Chenfei Ye ◽  
Xuan Zou ◽  
...  
Keyword(s):  
General Population ◽  
Disease Transmission ◽  
Clinical Severity ◽  
Transmission Risk ◽  
Contact Tracing ◽  
Initial Assessment ◽  
Reproductive Number ◽  
Risk Of Infection ◽  
The Impact ◽  
Close Contacts

AbstractBackgroundRapid spread of SARS-CoV-2 in Wuhan prompted heightened surveillance in Shenzhen and elsewhere in China. The resulting data provide a rare opportunity to measure key metrics of disease course, transmission, and the impact of control.MethodsThe Shenzhen CDC identified 391 SARS-CoV-2 cases from January 14 to February 12, 2020 and 1286 close contacts. We compare cases identified through symptomatic surveillance and contact tracing, and estimate the time from symptom onset to confirmation, isolation, and hospitalization. We estimate metrics of disease transmission and analyze factors influencing transmission risk.FindingsCases were older than the general population (mean age 45) and balanced between males (187) and females (204). Ninety-one percent had mild or moderate clinical severity at initial assessment. Three have died, 225 have recovered (median time to recovery is 21 days). Cases were isolated on average 4.6 days after developing symptoms; contact tracing reduced this by 1.9 days. Household contacts and those travelling with a case where at higher risk of infection (ORs 6 and 7) than other close contacts. The household secondary attack rate was 15%, and children were as likely to be infected as adults. The observed reproductive number was 0.4, with a mean serial interval of 6.3 days.InterpretationOur data on cases as well as their infected and uninfected close contacts provide key insights into SARS-CoV-2 epidemiology. This work shows that heightened surveillance and isolation, particularly contact tracing, reduces the time cases are infectious in the community, thereby reducing R. Its overall impact, however, is uncertain and highly dependent on the number of asymptomatic cases. We further show that children are at similar risk of infection as the general population, though less likely to have severe symptoms; hence should be considered in analyses of transmission and control.

scholarly journals Cumulative COVID-19 Disparities in Nursing Home: Focusing on Geographical Factors and Racial Components

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 869-870
Author(s):  
Sungjae Hong ◽  
Shannon Meija
Keyword(s):  
Nursing Home ◽  
Nursing Homes ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Disease Prevalence ◽  
Rural Nursing ◽  
Rural And Urban ◽  
Black Residents ◽  
The Impact ◽  
Over Time

Abstract The impact of COVID-19 has been greatest in vulnerable US populations. This study examines the cumulative geographical and racial disparities of COVID-19 cases in nursing homes. Analysis of COVID-19 Nursing Home Data from Centers for Medicare & Medicaid Services was limited to weekly reports from the nursing homes that reported the ratio of black residents, from 2020-05-31 to 2021-01-17 (N=268,222 from 8,026 nursing homes). The outcomes were weekly COVID-19 cases and death per 1,000 occupied beds. Nursing homes were categorized by a geographic (rural vs. urban) and racial composition (&gt;50% of residents are black vs. else). Elapsed time and county-level weekly COVID-19 cases and deaths/1,000 people were the key covariates. Multilevel zero-inflated negative binomial regression revealed evidence of cumulative COVID-19 disparity between rural and urban nursing homes. At the earliest time, COVID-19 incidence was lower in rural nursing homes than in urban nursing homes (IRR=0.406 for cases, 0.034 for death). The significant interaction with time implied that, over and above evolving disease prevalence, rural nursing homes became more likely than urban nursing homes to experience COVID-19 over time (IRR=1.057 for cases, 1.193 for death). Nursing homes, with &gt;50% black residents, were more likely to experience COVID-19 than their counterparts at the earliest time (IRR=1.339 for cases, 5.630 for death), but independent of local disease prevalence, this disparity decreased over time (IRR=0.973 for cases, 0.972 for death). Our findings suggest that racial and geographic factors contribute to the cumulation of disadvantage during the COVID-19 crisis at the second half of 2020.

Maturity of Judgment and Perceptual Deterrence

2018 ◽  
Vol 45 (11) ◽  
pp. 1762-1781 ◽  
Author(s):  
Heejin Lee ◽  
Christopher J. Sullivan ◽  
J. C. Barnes
Keyword(s):  
Individual Differences ◽  
Juvenile Offenders ◽  
Fixed Effects ◽  
Regression Models ◽  
Rate Of Change ◽  
Costs And Benefits ◽  
Perceptual Deterrence ◽  
Pathways To Desistance ◽  
The Impact ◽  
Over Time

Recent deterrence literature has found that the degree to which sanction threats are perceived to influence subsequent offending differs within individuals and between individuals over time. This study examines whether three psychosocial aspects (temperance, perspective, responsibility) relevant to the maturity of judgment predict within-individual and between-individual differences in levels of perceptual deterrence. Random effects regression models with fixed effects (hybrid models) are used to estimate the impact of maturity of judgment on the perceived risks, costs, and benefits of crime among a sample of serious juvenile offenders from the Pathways to Desistance study over 7 years of development. The results support both within-person effects and between-person effects. More mature judgment ability is generally associated with the perception of greater risks, heavier costs of punishment, and fewer rewards of crime. The rate of change in perceptual deterrence by maturity of judgment varies between individuals. Implications of the findings are discussed.

scholarly journals The First GAEN-Based COVID-19 Contact Tracing App in Norway Identifies 80% of Close Contacts in “Real Life” Scenarios

2021 ◽  
Vol 3 ◽  
Author(s):  
Hinta Meijerink ◽  
Camilla Mauroy ◽  
Mia Karoline Johansen ◽  
Sindre Møgster Braaten ◽  
Christine Ursin Steen Lunde ◽  
...  
Keyword(s):  
Operating Systems ◽  
Real Life ◽  
Contact Tracing ◽  
Lower Sensitivity ◽  
Life Test ◽  
Public Places ◽  
Per Se ◽  
The Impact ◽  
Test Scenarios ◽  
Close Contacts

The coronavirus disease 2019 (COVID-19) response in most countries has relied on testing, isolation, contact tracing, and quarantine (TITQ), which is labor- and time-consuming. Therefore, several countries worldwide launched Bluetooth-based apps as supplementary tools. The aim of using contact tracing apps is to rapidly notify people about their possible exposure to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and thus make the process of TITQ more efficient, especially upon exposure in public places. We evaluated the Norwegian Google Apple exposure notification (GAEN)-based contact tracing app Smittestopp v2 under relevant “real-life” test scenarios. We used a total of 40 devices, representing six different brands, and compared two different exposure configurations, experimented with different time thresholds and weights of the Bluetooth attenuation levels (buckets), and calculated the true notification rates among close contacts (≤2 m and ≥15 min) and false notification of sporadic contacts. In addition, we assessed the impact of using different operating systems and locations of the phone (hand/pocket). The best configuration tested to trigger exposure notification resulted in the correct notification of 80% of the true close contacts and incorrect notification of 34% of the sporadic contacts. Among those who incorrectly received notifications, most (67%) were within 2 m but the duration of contact was &lt;15 min and thus they were not, per se, considered as “close contacts.” Lower sensitivity was observed when using the iOS operating systems or carrying the phone in the pocket instead of in the hand. The results of this study were used to improve and evaluate the performance of the Norwegian contact-tracing app Smittestopp.

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